Development and validation of a novel EHR-based tumor progression outcome to support biomarker discovery.

Abstract

e19297 Background: Obtaining clinical outcomes for analysis has historically been a critical barrier to cancer genomics research. EHRs could constitute an important data source to bridge this gap, but EHRs rarely capture structured outcomes such as cancer progression. Novel, robust methods are needed to capture clinically relevant outcomes from EHRs. Methods: Among patients with lung adenocarcinoma whose tumors were sequenced via the Dana Farber Cancer Institute/Brigham and Women’s PROFILE study from 2013-2018, imaging reports following first palliative-intent systemic therapy were annotated using natural language processing (NLP) models trained to capture cancer progression according to the structured “PRISSMM” framework. NLP-based cancer progression and imaging report frequency were jointly modeled using inverse-intensity weighted generalized estimated equations, censored at six months, to explore associations between alterations in lung cancer biomarkers (ALK, EGFR, ROS1, BRAF, KRAS, SMARCA4) and progression. Among patients with KRAS mutations who received immunotherapy, we also analyzed the association between STK11 mutations and progression. The novel outcome generated by the model – imaging report-based progression (iPROG) – corresponded to the difference in the mean log odds of progression per inverse-intensity weighted report associated with a given biomarker; it was reported as adjusted mean probability and in exponentiated form as an odds ratio (OR). Results: Among 690 patients with lung adenocarcinoma, associations between tumor mutations and the iPROG outcome are listed in the Table. Conclusions: A deep NLP model applied to EHR data can capture a novel cancer progression outcome, which is associated with known prognostic markers in lung cancer. Application of this method to large “real world” datasets, with attention to interactions between treatment and genomics, could speed biomarker discovery. [Table: see text]

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

“…In oncology, recent attention has been paid to extraction of relevant medical details from free text (named entity extraction) 11 and predicting outcomes from clinical documents. [16][17][18] Table 1 describes low-and high-level NLP tasks that are likely to be incorporated into clinical NLP technologies in the future.…”

“…There is burgeoning research on extracting information about treatment [86][87][88] and outcomes or adverse events, both of which have potential value for research and direct patient care. [16][17][18][89][90][91][92] Extracting this information is more challenging because it generally is not reported in a structured way or with standardized language. Treatment regimens are often described differently among providers and institutions using differing investigational, generic, and brand names with frequent abbreviations and acronyms.…”